Field of the Subject Disclosure
The present subject disclosure relates to imaging for medical diagnosis. More particularly, the present subject disclosure relates to a method for analyzing a stained biopsy or surgical tissue sample being implemented by an image processing system and quality control of automated whole-slide analysis of tissue specimens.
Background of the Subject Disclosure
In the field of digital pathology, biological specimens such as tissue sections, blood, cell cultures and the like may be stained with one or more stains and analyzed by viewing or imaging the stained specimen. Observing the stained specimen, in combination with additional clinical information, enables a variety of processes, including diagnosis of disease, prognostic and/or predictive assessment of response to treatment, and assists in development of new drugs to fight disease. As used herein, a “target” or “target object” is a feature of the specimen that a stain identifies. A target or target object may be a protein, protein fragment, nucleic acid, or other object of interest recognized by an antibody, a molecular probe, or a non-specific stain. Those targets that are specifically recognized may be referred to as biomarkers in this subject disclosure. Some stains do not specifically target a biomarker (e.g. the often used counterstain hematoxylin). While hematoxylin has a fixed relationship to its target, most biomarkers can be identified with a user's choice of a stain. That is, a particular biomarker may be visualized using a variety of stains depending on the particular needs of the assay. Subsequent to staining, the assay may be imaged for further analysis of the contents of the tissue specimen. An image of an entire slide is typically referred to as a whole-slide image, or simply whole-slide.
Quantitative analysis of a whole-slide, such as counting target objects such as cells of a certain kind, or the quantitation of a staining response for all cells on a slide, is not feasible for human observers. Typically, a whole-slide contains several thousand to several hundred thousand cells, of which all or just a fraction may be relevant for an analysis question at hand. Methods from image analysis, computer vision, and pattern recognition can be used for an automated quantitative analysis. Such automation with computer software enables quantitative whole-slide analyses.
Current implementations of whole-slide analyses allow the user to select a number of FOVs for quality control (QC). These FOVs are randomly selected or systematically sampled by the software from all FOVs in the analyzed tissue. A system for automated whole-slide analysis may present a low-magnification view of the whole tissue that shows the position of FOVs for QC. For example, FIG. 1A depicts a low-magnification view, with red rectangles 101 depicting positions of FOVs. For each of these positions 101, an FOV image of higher magnification may be generated as shown in FIG. 1B. A disadvantage of this method is that not all the tissue is presented for QC, only the selected FOVs. Errors of the algorithm in tissue regions that are not visible in the presented FOVs cannot be detected by the observer. Even if QC images and FOV selections are interactively generated, for instance by presenting a low magnification image such as the one in FIG. 1A in a graphical user interface (GUI) enabling a user to select (for example with a mouse click) a region of interest, this process is also tedious and requires the observer to interact with the GUI until she or he is satisfied that all relevant regions of tissue have been observed.